STATS 726 : Time Series


2022 Semester Two (1225) (15 POINTS)

Course Prescription

Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.

Course Overview

STATS 726 provides a general introduction to the theory of time series and prediction including stationary processes, moving average and autoregressive models (ARMA), modeling and estimation in the time domain, seasonal models, non-stationary models, forecasting, and spectral analysis. This foundation course is particularly suitable for students in statistics, economics and finance, and in the engineering and physical sciences.

The skills learned in the course are particularly useful for data scientists working on the forecasting of time series. The course also provides a solid theoretical background for the students who plan to continue their postgraduate studies.

Course Requirements

Prerequisite: STATS 210, and 320 or 325

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Graduate Profile: Master of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Identify the main components and features present in a time series (Capability 1, 2 and 3)
  2. Describe the theory behind stationary processes (Capability 1, 2, 3 and 4)
  3. Apply (seasonal) autoregressive (integrated) moving average models to the analysis of univariate time series (Capability 1, 2 and 3)
  4. Perform time-series forecasting (Capability 1, 2 and 3)
  5. Use R for the analysis and prediction of time series (Capability 1, 2 and 3)
  6. Perform inference in time/frequency domain (Capability 1, 2 and 3)
  7. Communicate the solution to a time series problem with reference to the context of the problem (Capability 1 and 4)


Assessment Type Percentage Classification
4 Assignments 40% Individual Coursework
Mid-term Test 20% Individual Coursework
Final Exam 40% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
4 Assignments
Mid-term Test
Final Exam

Key Topics

Linear processes; ARMA models; non-stationary models; prediction for time series models; inference in the time/frequency domain

Special Requirements

The mid semester online test will be held in the evening. The date and time will be advised on Canvas.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 12.5 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 36 hours of lectures, 48 hours of reading and thinking about the content, and 66 hours of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

The course will not include live online events. The course is available for students who are remote. 

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Slides provided by the lecturer. The following books might be helpful:
1. R. H. Shumway and D. S. Stoffer, "Time series analysis and its applications. With R examples", Springer 2011.
2. P. J. Brockwell and R. A. Davis, "Introduction to time series analysis and forecasting", Springer 2002.
3. P. Bloomfield, "Fourier analysis of time series. An introduction", John Wiley and Sons 2000.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 11/11/2021 09:29 a.m.